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Discovering tomorrow's global healthtech trends today

intech.media

Discovering tomorrow's global healthtech trends today

How AI is Transforming Public Transit in Smart Cities 

Artificial intelligence is a revolutionizing concept that can lead to positive change and foster sustainable transitions to a more resource-efficient paradigm. One of the major sectors that AI can massively change is transport.  

The employment of intelligent transport systems can improve mobility provision and its impact on urban development in general and automated transport. Many governments have set up traffic control systems in congested cities.  

We often rely on human input to monitor traffic flow, the frequency of public trains and buses, etc. Yet, there are always limitations to deal with. It is where advanced technologies step in to make transit infrastructure more robust and mitigate the risks for a safer, secure, and efficient transit. 

 

Connected and Autonomous Vehicles (CAVs)  

Automated driving is broadly seen as a technology that could signal an evolution towards a major mobility transformation. CAVs are one of the leading AI applications in transport; AI algorithms and machine learning significantly decrease human intervention and drive CAVs.  

Connected and autonomous vehicles have been hitting the headlines. This technology can be handy for the transportation of goods and public transit. CAVs enable various advantages, including optimizing driving time, improving traffic safety and accident prevention, diminishing road traffic congestion, increasing comfort, reducing environmental degradation, air pollution, noise nuisance, and social exclusion for those currently unable to drive.   

Companies like Audi, BMW, Ford, Mercedes-Bosch alliance, Microsoft, etc., actively develop CAVs and conduct testing experiments with different success levels. Industry giants like Google and Uber, non-typical vehicle manufacturers, will radically change the industry’s dynamics, growing new players in a more versatile automotive market. 

On the other hand, autonomous vehicles need responsive, secure, and safe solutions to make a split-second decision based on a detailed understanding of the surrounding. For this, it needs a large amount of data to understand its environment and make the right decision at the right time. 

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Autonomous Personal, Unmanned Aerial Vehicles, and Computer Vision. 

We use autonomous personal and Unmanned Aerial Vehicles for a variety of purposes. When UAVs were developed, they were manually controlled using a remote. However, after the incorporation of Artificial Intelligence, all of their operations are automated. 

One of the good examples of UAVs is drones. Like Autonomous vehicles, drones also use sensors to collect data of the surrounding. This data is then used for assisted flights, accessibility, and making operations more straightforward. 

Unmanned Aerial Vehicles enable improved solutions towards the provision of the advanced military, policing, and commercial services. Notably, UAVs can perform tasks related to intelligence, surveillance and reconnaissance, target identification and designation, civil security control, environmental monitoring, surveying, and geospatial activities, weather monitoring, and meteorology, forest fire detection, traffic control. Many of these operations reflect the day-to-day functionality of a smart city.  

These technologies are rising models of mobility that promise to combine the best of ground-based and air-based transportation to reduce urban congestion and use of free space in the air. 

Computer vision also becomes handy in identifying, tracking, and classifying a target using UAVs and PAVS. Furthermore, it helps the drones avoid collisions during flight so they can fly safely without a human’s help. 

A blend of image recognition and machine learning enables the detection of traffic jams, weather patterns, parking violations, and more. Examples of practical applications are limitless, and the data originating from Computer Vision can be enhanced with other data that comes from external systems. 

 

Mobility-as-a-Service (MaaS) 

Mobility-as-a-Services is an on-demand transport solution. It means that instead of owning your vehicle, you can order when and where you want. This concept is very much the same as Spotify and Netflix that provide access to movies, music, and TV. There are various kinds of transport options available. Uber, Rental services, etc., are all examples of MaaS. 

A recent report from the UN estimates that by 2050 half of the world population will live in urban areas. This requires solid planning for transport infrastructure. No matter how many roads we can build, there is a limit on how many vehicles can be on the road at one time. 

If you live in an urban area, you must have noticed that more people come to the cities every year to find new opportunities. No matter how much the government increases the budget for highways and roads, we will see road jams due to traffic one day or another. Therefore, the cities need to take an efficient approach to transport infrastructure. 

AI can help with mobility as a service to mitigate the problems. It will be easier for an individual to get a ride without the hassle of owning a vehicle. A person can go to work in his car or can use public transport. In smart cities, MaaS can reduce the number of cars on the road because most people will be using shared transport instead of driving their vehicle. 

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Internet of Things (IoT) 

Whenever smart cities’ concepts arise, they get followed by IoT. Although there is no standard understanding of what the IoT incorporates, the IoT grew as a communication model that envisions a future in which the objects of everyday life will be able to interact and communicate with one another and with the users as they become an indispensable part of the Internet.  

The IoT is at the core of the smart city’s development and what makes it possible; smart cities need to have three key features that the IoT can provide: intelligence, interconnection, and instrumentation.  

When fully operational and able to maximize its potential, the IoT will unite the different AI-centric transport ecosystem components. 

AI and IoT can work together in a smart city to ensure that citizens can transit from one place to another as safely and efficiently as possible. It is essential to manage the flow of traffic in a congested city. One example is Los Angeles that has implemented such a solution to control traffic flow. They have installed cameras and sensors that keep the central traffic management system with real-time updates. All the data comes from sensors and cameras and gets analyzed in real-time. The city had also installed a new transportation controller two years ago in 2018. 

Smart Parking is also becoming easier with the combination of AI and IoT. Finding a place for parking your car is a significant issue in urban areas, especially in big crowded cities. It usually takes from minutes to an hour to find a parking spot. This is where smart parking comes in to help find parking slots.  

 

Conclusion 

If used responsibly, advanced technologies like artificial intelligence and machine learning are powerful tools, with the potential to enable massive positive changes and promote sustainable shifts to a more resource-efficient livability model. These technologies can be used as solutions for improved problem-solving. Especially in reforming urban landscapes and establishing a new era, the era of the “smart city.” Integrating Artificial intelligence brings a plethora of opportunities and a wide range of beneficial applications. 

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